The authors suggest a computationally efficient multimodal deep learning framework using Bidirectional Encoder Representations of Transformers (BERT) to extract textual features and convolutional neural networks to learn visual representations that offers a computational scaling alternative to attention-based models, w...
Results indicate that contextual representations from BERT, sequential dependency modelling through LSTM, and adaptive feature weighting through Bahdanau Attention provide complementary capabilities for fake news classification.
The proliferation of social media has created fertile ground for misinformation, a challenge further intensified by recent advances in generative artificial intelligence. Modern fake news increasingly takes the form of sophisticated multimodal campaigns, where synthetic images and stylistically manipulated text are joi...
A hybrid ensemble learning framework enhanced by RoBERTa-based feature representation and optimized using the Whale Optimization Algorithm is introduced to effectively capture deep semantic patterns in textual data while improving classification performance through adaptive parameter tuning.
The widespread use of social media and online news platforms has significantly increased the dissemination of misinformation, creating a growing need for reliable fake news detection systems. Although traditional machine learning techniques provide efficient classification, they often struggle to capture contextual mea...
The results indicate that explicitly modeling semantic conflict as a discriminative feature effectively improves detection precision and generalization, providing a robust solution for factual verification in complex media environments.
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